Image processing apparatus, image processing method, and computer readable recording medium for correcting pixel value of pixel-of-interest based on correction amount calculated

ABSTRACT

An image processing apparatus includes: an acquisition unit configured to obtain image data generated by an image sensor forming an array pattern using color filters having mutually different spectral transmittances and to obtain a correction coefficient for correcting a difference between pixel values corresponding to a difference between a spectral sensitivity and a preset reference spectral sensitivity in a wavelength range in a pixel-of-interest; a correction amount calculation unit configured to calculate an estimation value of a color component in the pixel-of-interest using a pixel value of each of pixels in surrounding pixels of a same color and the correction coefficient and configured to calculate a correction amount of the pixel value of the pixel-of-interest based on the estimation value and the correction coefficient of the pixel-of-interest; and a pixel value correction unit configured to correct the pixel value of the pixel-of-interest based on the calculated correction amount.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT International Application No.PCT/JP2015/079896 filed on Oct. 22, 2015, the entire contents of whichare incorporated herein by reference.

BACKGROUND

The present disclosure relates to an image processing apparatus, animage processing method, and a computer readable recording medium.

In imaging apparatuses such as digital cameras, there is a knowntechnique of adjusting, for each image sensor, variations in spectralsensitivity of pixels due to variations in spectral transmittance of acolor filter provided on a light receiving surface of an image sensorsuch as a charge coupled device (CCD) and a complementary metal oxidesemiconductor (CMOS) (refer to JP 2010-193378 A). In this technique,visible light spectrally split by a prism is photoelectrically convertedby an image sensor to calculate values of red (R), green (G), and blue(B) components from image data including a combination of these colorcomponents of R, G, and B for each of wavelengths, and then, acorrection coefficient to be multiplied to each of the R, G, and Bvalues so as to decrease a difference between each of the calculated R,G, and B values and a reference value calculated beforehand for each ofwavelengths, and thereafter the variation in the spectral sensitivityfor each of image sensors is corrected using the calculated correctioncoefficient.

SUMMARY

An image processing apparatus according to one aspect of the presentdisclosure includes: an acquisition unit configured to obtain image datagenerated by an image sensor that forms a predetermined array patternusing color filters of a plurality of colors having mutually differentspectral transmittances and in which each of the color filters isarranged at a position corresponding to any of a plurality of pixels andto obtain a correction coefficient from a recording unit configured torecord, for each of the pixels, the correction coefficient forcorrecting a difference between pixel values corresponding to adifference between a spectral sensitivity and a preset referencespectral sensitivity in a predetermined wavelength range in apixel-of-interest; a correction amount calculation unit configured tocalculate an estimation value of a color component as a correctiontarget in the pixel-of-interest using a pixel value of each of pixels insurrounding pixels of a same color and the correction coefficient, thesurrounding pixel of a same color being a pixel surrounding thepixel-of-interest and being a pixel including a color filter having asame color as a color filter arranged on the pixel-of-interest, andconfigured to calculate a correction amount of the pixel value of thepixel-of-interest based on the estimation value and the correctioncoefficient of the pixel-of-interest; and a pixel value correction unitconfigured to correct the pixel value of the pixel-of-interest based onthe correction amount calculated by the correction amount calculationunit.

The above and other features, advantages and technical and industrialsignificance of this disclosure will be better understood by reading thefollowing detailed description of presently preferred embodiments of thedisclosure, when considered in connection with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram schematically illustrating a configuration ofan imaging system according to a first embodiment;

FIG. 2 is a flowchart illustrating an outline of processing executed byan image processing apparatus according to the first embodiment;

FIG. 3 is a flowchart illustrating an outline of correction amountcalculation processing in FIG. 2;

FIG. 4 is a flowchart illustrating an outline of R component correctionamount calculation processing in FIG. 3;

FIG. 5 is a flowchart illustrating an outline of G component correctionamount calculation processing in FIG. 3;

FIG. 6 is a flowchart illustrating an outline of B component correctionamount calculation processing in FIG. 3;

FIG. 7 is a flowchart illustrating an outline of R component estimationvalue calculation processing based on average in FIG. 4;

FIG. 8 is a flowchart illustrating an outline of R component estimationvalue calculation processing based on a similarity level in FIG. 4;

FIG. 9 is a diagram schematically illustrating a method for calculatingan R component estimation value calculated by a correction amountcalculation unit of the image processing apparatus according to thefirst embodiment;

FIG. 10 is a flowchart illustrating an outline of similarity levelcalculation processing in individual R component candidate values inFIG. 8;

FIG. 11 is a flowchart illustrating an outline of correction amountcalculation processing for a pixel value of a pixel-of-interest (x, y)in FIG. 3;

FIG. 12 is a flowchart illustrating an outline of a correction amountcalculation processing for a pixel value of a pixel-of-interest (x, y)according to a modification of the first embodiment;

FIG. 13 is a flowchart illustrating an outline of R component correctionamount calculation processing executed by an image processing apparatusaccording to a second embodiment;

FIG. 14 is a flowchart illustrating an outline of R component estimationvalue calculation processing based on a ratio of a pixel value to thecorrection coefficient in FIG. 13;

FIG. 15 is a diagram schematically illustrating distribution ofcorrection coefficients similar to each other;

FIG. 16 is a flowchart illustrating an outline of correction amountcalculation processing executed by an image processing apparatusaccording to a third embodiment;

FIG. 17 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing in FIG. 16;

FIG. 18 is a flowchart illustrating an outline of different-colorcomponent estimation value calculation processing based on a similaritylevel for different-color components of the pixel-of-interest (x, y) inFIG. 17;

FIG. 19 is a flowchart illustrating an outline of similarity level Sim′calculation processing in candidate values EstC1′ and EstC2′ in FIG. 18;

FIG. 20 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing executed by an imageprocessing apparatus according to a fourth embodiment;

FIG. 21 is a flowchart illustrating an outline of individual colorcomponent estimation value calculation processing based on a similaritylevel for individual color components in FIG. 20;

FIG. 22 is a flowchart illustrating an outline of the similarity levelSim′ calculation processing in candidate values EstR′, EstG′, and EstB′in FIG. 21;

FIG. 23 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing executed by an imageprocessing apparatus according to a fifth embodiment;

FIG. 24 is a flowchart illustrating an outline of different-colorcomponent estimation value calculation processing based on simultaneousequations in FIG. 23;

FIG. 25 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing executed by an imageprocessing apparatus according to a sixth embodiment;

FIG. 26 is a flowchart illustrating an outline of different-colorcomponents estimation value calculation processing based on a ratio of apixel value to the correction coefficient in FIG. 25; and

FIG. 27 is a diagram schematically illustrating a calculation region ofthe ratio Ratio in a case where a different-color pixel in the vicinityof the pixel-of-interest (x, y) is saturated.

DETAILED DESCRIPTION

Hereinafter, embodiments will be described with reference to thedrawings. Note that the present disclosure is not limited by thefollowing embodiments. In the description of the drawings, the sameportions are given the same reference numerals.

First Embodiment

Configuration of imaging system FIG. 1 is a block diagram schematicallyillustrating a configuration of an imaging system according to a firstembodiment. An imaging system 1 illustrated in FIG. 1 includes animaging apparatus 10, an image processing apparatus 40, and a displaydevice 50.

Configuration of Imaging Apparatus

First, a configuration of the imaging apparatus 10 will be described.The imaging apparatus 10 includes an optical system 101, a diaphragm102, a shutter 103, a driver 104, an image sensor 105, a color filter106, an analog processing unit 107, an A/D converter 108, a firstoperating unit 109, a memory I/F unit 110, a recording medium 111, avolatile memory 112, a non-volatile memory 113, a bus 114, an imagingcontroller 115, and a first external I/F unit 116.

The optical system 101 includes one or more lenses. The optical system101 includes a focus lens and a zoom lens, for example.

The diaphragm 102 adjusts exposure by limiting an incident amount oflight collected by the optical system 101. Under the control of theimaging controller 115, the diaphragm 102 limits the incident amount ofthe light collected by the optical system 101. The incident amount oflight may be controlled using the shutter 103 and an electronic shutterin the image sensor 105 without using the diaphragm 102.

Under the control of the imaging controller 115, the shutter 103 sets astate of the image sensor 105 to an exposure state or a light shieldingstate. The shutter 103 includes a focal plane shutter, for example. Itis allowable to use the electronic shutter in the image sensor 105instead of the shutter 103.

Under the control of the imaging controller 115 described below, thedriver 104 drives the optical system 101, the diaphragm 102, and theshutter 103. For example, the driver 104 performs zoom magnificationchange or focusing position adjustment for the imaging apparatus 10 bymoving the optical system 101 along an optical axis O1.

Under the control of the imaging controller 115 described below, theimage sensor 105 receives the light collected by the optical system 101,converts the received light into image data (electric signal), andoutputs the image data. The image sensor 105 is formed with an imagesensor such as complementary metal oxide semiconductor (CMOS) or acharge coupled device (CCD) in which a plurality of pixels istwo-dimensionally arranged. Moreover, the image sensor 105 has anelectronic shutter function capable of electronically controlling theamount of received light.

The color filter 106 is stacked on a light receiving surface of theimage sensor 105. The color filter 106 is configured to allow aplurality of color filters transmitting light having mutually differentwavelength regions to form a predetermined array pattern, and each ofthe color filters with this array pattern is arranged at a positioncorresponding to any of the plurality of pixels constituting the imagesensor 105. The color filter 106 includes, in a Bayer array, a filter Rtransmitting light in the red wavelength region, a filter G transmittinglight in the green wavelength region, and a filter B transmitting lightin the blue wavelength region, each being arranged on the lightreceiving surface of each of the pixels of the image sensor 105. In thefollowing description, a pixel on which the filter R is arranged on thelight receiving surface is referred to as an R-pixel, a pixel on whichthe filter G is arranged on the light receiving surface is referred toas a G-pixel, and a pixel on which the filter B is arranged on the lightreceiving surface is referred to as a B-pixel.

The analog processing unit 107 performs predetermined analog processingonto an analog signal output from the image sensor 105 and outputs theprocessed signal to the A/D converter 108. Specifically, the analogprocessing unit 107 performs noise reduction processing, gain-upprocessing, or the like, on the analog signal input from the imagesensor 105. For example, the analog processing unit 107 performs, ontothe analog signal, reduction of reset noise, etc. and waveform shaping,and then, further performs gain-up so as to achieve intended brightness.

The A/D converter 108 generates digital image data (hereinafter,referred to as “RAW image data”) by performing A/D conversion onto theanalog signal input from the analog processing unit 107, and outputs thegenerated data to the volatile memory 112 via the bus 114. Note that theA/D converter 108 may directly output the RAW image data to each ofportions of the imaging apparatus 10 described below. Note that it isallowable to configure such that the color filter 106, the analogprocessing unit 107 and the A/D converter 108 are provided on the imagesensor 105, and that the image sensor 105 directly outputs digital RAWimage data.

The first operating unit 109 provides various instructions to theimaging apparatus 10. Specifically, the first operating unit 109includes a power switch, a release switch, an operation switch, and amoving image switch. The power switch switches the power supply statesof the imaging apparatus 10 between an on-state and an off-state. Therelease switch provides an instruction of still image shooting. Theoperation switch switches various settings of the imaging apparatus 10.The moving image switch provides an instruction of moving imageshooting.

The recording medium 111 includes a memory card attached from outside ofthe imaging apparatus 10, and is removably attached onto the imagingapparatus 10 via the memory I/F unit 110. Moreover, the recording medium111 may output programs and various types of information to thenon-volatile memory 113 via the memory I/F unit 110 under the control ofthe imaging controller 115.

The volatile memory 112 temporarily stores image data input from the A/Dconverter 108 via the bus 114. For example, the volatile memory 112temporarily stores image data sequentially output from the image sensor105 frame by frame, via the analog processing unit 107, the A/Dconverter 108, and the bus 114. The volatile memory 112 includes asynchronous dynamic random access memory (SDRAM).

The non-volatile memory 113 records various programs needed to operatethe imaging apparatus 10 and various types of data used in execution ofthe program. The non-volatile memory 113 includes a program recordingunit 113 a and a correction coefficient recording unit 113 b configuredto record a correction coefficient for correcting variations in spectralsensitivity of each of the plurality of pixels constituting the imagesensor 105, input via the first external I/F unit 116. Note that thecorrection coefficient is a coefficient for correcting a differencebetween the pixel values corresponding to the difference between thespectral sensitivity and the preset reference spectral sensitivity in apredetermined wavelength range on a pixel-of-interest. The referencespectral sensitivity is an average spectral sensitivity of pixels havinga same color in each of the color filters when uniform light is appliedto the image sensor 105. This correction coefficient is calculatedbeforehand by an apparatus (not illustrated) and recorded in thecorrection coefficient recording unit 113 b.

The bus 114 includes a transmission line that connects individualcomponents of the imaging apparatus 10 with each other, and transfersvarious types of data generated inside the imaging apparatus 10 to eachof the individual components of the imaging apparatus 10.

The imaging controller 115 includes a central processing unit (CPU), andintegrally controls operation of the imaging apparatus 10 by providinginstruction and transferring data to individual components of theimaging apparatus 10 in response to an instruction signal and a releasesignal from the first operating unit 109. For example, in a case where asecond release signal has been input from the first operating unit 109,the imaging controller 115 performs control of starting shootingoperation on the imaging apparatus 10. Herein, the shooting operation onthe imaging apparatus 10 is operation of predetermined processingperformed by the analog processing unit 107 and the A/D converter 108,onto an analog signal output by the image sensor 105. The image dataprocessed in this manner are recorded in the recording medium 111 viathe bus 114 and the memory I/F unit 110 under the control of the imagingcontroller 115.

The first external I/F unit 116 outputs information input from externalapparatuses via the bus 114, to the non-volatile memory 113 or thevolatile memory 112, and together with this, outputs, to externalapparatuses via the bus 114, information stored in the volatile memory112, information recorded in the non-volatile memory 113, and the imagedata generated by the image sensor 105. Specifically, the first externalI/F unit 116 outputs the image data generated by the image sensor 105and the correction coefficient recorded in the correction coefficientrecording unit 113 b, to the image processing apparatus 40 via the bus114.

Configuration of Image Processing Apparatus

Next, a configuration of the image processing apparatus 40 will bedescribed.

The image processing apparatus 40 includes a second external I/F unit41, a second recording unit 42, a bus 43, a spectral sensitivityvariation correction unit 44, and an image processing unit 45.

The second external I/F unit 41 obtains the image data generated by theimage sensor 105 and the correction coefficient recorded by thecorrection coefficient recording unit 113 b individually via the firstexternal I/F unit 116 of the imaging apparatus 10, and outputs theobtained image data and correction coefficients to the spectralsensitivity variation correction unit 44 or the second buffer unit 422.The second external I/F unit 41 and the first external I/F unit 116 aremutually connected via a control cable, a wireless communication path,or the like, capable of bidirectionally exchanging information. Thesecond external I/F unit 41 functions as an acquisition unit accordingto the first embodiment.

The second recording unit 42 includes a volatile memory and anonvolatile memory, and records image data input from the imagingapparatus 10 via the second external I/F unit 41, correctioncoefficients, and various programs needed for operation of the imageprocessing apparatus 40, and various data to be used during execution ofthe program. The second recording unit 42 further includes a secondprogram recording unit 421 that records a program for driving the imageprocessing apparatus 40, and a second buffer unit 422 temporarilystoring the image data and the correction coefficient of thepixel-of-interest input from the imaging apparatus 10.

The bus 43 includes a transmission line connecting individual componentsof the image processing apparatus 40 with each other, and transfersvarious types of data generated inside the image processing apparatus 40to each of the individual components of the image processing apparatus40.

The spectral sensitivity variation correction unit 44 correctsvariations in the spectral sensitivity of each of the pixels of theimage corresponding to the image data obtained by the second externalI/F unit 41, and outputs the corrected data to the image processing unit45. The spectral sensitivity variation correction unit 44 includes acorrection amount calculation unit 441 and a pixel value correction unit442. Note that the spectral sensitivity variation correction unit 44 mayperform optical black (OB) subtraction processing in a case where anoptical black (OB) value is contained in the image data.

The correction amount calculation unit 441 calculates an estimationvalue of a color component as a correction target in thepixel-of-interest using a correction coefficient in thepixel-of-interest, recorded by the correction coefficient recording unit113 b, and using the pixel value of each of the pixels in surroundingpixels of a same color around the pixel-of-interest, the surroundingpixel of a same color being a pixel surrounding the pixel-of-interestand being a pixel including a color filter having a same color as acolor filter arranged on the pixel-of-interest, and then, calculates acorrection amount of the pixel value of the pixel-of-interest based onthe estimation value and the correction coefficient of thepixel-of-interest. Here, the surrounding pixel of a same color is apixel located in the vicinity of the pixel-of-interest and having acolor filter of the same color as the color of the color filter arrangedon the pixel-of-interest.

The pixel value correction unit 442 corrects the pixel value of thepixel-of-interest using the correction amount calculated by thecorrection amount calculation unit 441. Specifically, the pixel valuecorrection unit 442 corrects the pixel value of the pixel-of-interest bysubtracting the correction amount calculated by the correction amountcalculation unit 441 from the pixel value of the pixel-of-interest, andoutputs the corrected pixel value to the image processing unit 45.

The image processing unit 45 performs predetermined image processingonto the image data in which spectral sensitivity variation has beencorrected by the spectral sensitivity variation correction unit 44, andoutputs the processed image data to the display device 50. Thepredetermined image processing herein corresponds to basic imageprocessing including white balance adjustment processing, and includingsynchronization processing of the image data, color matrix calculationprocessing, y correction processing, color reproduction processing, edgeenhancement processing, and noise reduction processing in a case wherethe image sensor 105 is arranged in a Bayer array. Moreover, the imageprocessing unit 45 performs image processing of reproducing a naturalimage based on individual image processing parameters that have been setbeforehand. The parameters of image processing are values of contrast,sharpness, saturation, white balance, and gradation. Note that the imagedata that has undergone predetermined image processing may be recordedin the non-volatile memory 113 or the recording medium 111 of theimaging apparatus 10 via the second external I/F unit 41.

Configuration of Display Device

Next, a configuration of the display device 50 will be described. Thedisplay device 50 displays an image that corresponds to the image datainput from the image processing apparatus 40. The display device 50includes a display panel of liquid crystal, organic electroluminescence(EL).

In the imaging system 1 having the above configuration, the imageprocessing apparatus 40 obtains each of the image data and thecorrection coefficient from the imaging apparatus 10 and calculates acorrection amount for correcting the pixel value of thepixel-of-interest of the image data using the obtained correctioncoefficient, and thereafter, the pixel value of the pixel-of-interest iscorrected using the calculated correction amount. The display device 50displays an image that corresponds to the image data image-processed bythe image processing apparatus 40.

Processing on Image Processing Apparatus

Next, processing executed by the image processing apparatus 40 will bedescribed. FIG. 2 is a flowchart illustrating an outline of processingexecuted by the image processing apparatus 40, illustrating a mainroutine.

As illustrated in FIG. 2, the correction amount calculation unit 441first obtains, from the imaging apparatus 10, image data generated bythe image sensor 105 and the correction coefficient recorded by thecorrection coefficient recording unit 113 b, and executes correctionamount calculation processing of calculating the correction amount ofthe pixel value of the pixel-of-interest in the obtained image data(step S1). In this case, the correction amount is a signed value.Specifically, the correction amount is set to a negative value for apixel in which the spectral sensitivity is lower than the referencespectral sensitivity, and is set to a positive value for a pixel inwhich the spectral sensitivity is higher than the reference spectralsensitivity. Details of the correction amount calculation processingwill be described below. Note that in a case where the definitions ofpositive and negative of the correction amount are reversed, theaddition and subtraction may be reversed in the processing using thecorrection coefficient described below.

The pixel value correction unit 442 corrects the pixel value of each ofthe pixels by subtracting the correction amount of each of the pixelscalculated by the correction amount calculation unit 441 (step S2).After step S2, the image processing apparatus 40 finishes the currentprocessing.

Correction Amount Calculation Processing

Next, details of the correction amount calculation processing describedin step S1 of FIG. 2 will be described. FIG. 3 is a flowchartillustrating an outline of the correction amount calculation processing.

As illustrated in FIG. 3, the correction amount calculation unit 441first initializes (step S11) the counter y (counter y=0) indicating theposition of the pixel in a height direction (vertical direction) of theimage corresponding to the image data stored in the second buffer unit422 of the second recording unit 42, and then, initializes (step S12) acounter x (counter x=0) indicating the position of the pixel in a widthdirection (horizontal direction) of the image corresponding to the imagedata.

Subsequently, the correction amount calculation unit 441 executes Rcomponent correction amount calculation processing (step S13) ofcalculating the correction amount of the R component for the pixel valueat the pixel-of-interest (x, y). Note that the R component is a pixelvalue of the R wavelength band. Details of the R component correctionamount calculation processing will be described below.

Subsequently, the correction amount calculation unit 441 executes Gcomponent correction amount calculation processing (step S14) ofcalculating the correction amount of the G component for the pixel valueat the pixel-of-interest (x, y). Note that the G component is a pixelvalue of the G wavelength band. Details of the G component correctionamount calculation processing will be described below.

Subsequently, the correction amount calculation unit 441 executes Bcomponent correction amount calculation processing (step S15) ofcalculating the correction amount of the B component for the pixel valueat the pixel-of-interest (x, y). Note that the B component is a pixelvalue of the B wavelength band. Details of the B component correctionamount calculation processing will be described below.

Thereafter, the correction amount calculation unit 441 executescorrection amount calculation processing (step S16) of calculating thecorrection amount for the pixel value of the pixel-of-interest (x, y)based on the correction amount of each of the R component, the Gcomponent, and the B component respectively calculated in theabove-described steps S13 to S15. The details of the correction amountcalculation processing for the pixel-of-interest (x, y) will bedescribed below.

Subsequently, the correction amount calculation unit 441 increments thecounter x as (x=x+1) (step S17).

Thereafter, in a case where the counter x is smaller than the width ofthe image corresponding to the image data (step S18: Yes), thecorrection amount calculation unit 441 returns to step S13. In contrast,in a case where the counter x is not smaller than the width of the imagecorresponding to the image data (step S18: No), the correction amountcalculation unit 441 proceeds to step S19 described below.

In step S19, the correction amount calculation unit 441 increments thecounter y as (y=y+1).

Thereafter, in a case where the counter y is smaller than the height ofthe image corresponding to the image data (step S20: Yes), the imageprocessing apparatus 40 returns to the above-described step S12. Incontrast, in a case where the counter y is not smaller than the heightof the image corresponding to the image data (step S20: No), the imageprocessing apparatus 40 returns to the main routine in FIG. 2.

R Component Correction Amount Calculation Processing

Next, details of the R component correction amount calculationprocessing described in step S13 of FIG. 3 will be described. FIG. 4 isa flowchart illustrating an outline of R component correction amountcalculation processing.

As illustrated in FIG. 4, in a case where the pixel-of-interest (x, y)is an R pixel (step S31: Yes), the correction amount calculation unit441 first executes R component estimation value calculation processingbased on average (step S32), that is, processing of calculating anestimation value of the R component based on an average value of thepixel values of the pixels surrounding the pixel-of-interest (x, y) (forexample, 5×5 pixel range around the pixel-of-interest (x, y) as acenter). Details of the R component estimation value calculationprocessing based on average will be described below. After step S32, theimage processing apparatus 40 proceeds to step S34.

In step S31, in a case where the pixel-of-interest (x, y) is not an Rpixel (step S31: No), the correction amount calculation unit 441executes R component estimation value calculation processing based on asimilarity level (step S33), that is, processing of calculating theestimation value of the R component based on a similarity level of thepixel value of the pixel-of-interest (x, y). Details of the R componentestimation value calculation processing based on a similarity level willbe described below. After step S33, the image processing apparatus 40proceeds to step S34.

Subsequently, the correction amount calculation unit 441 multiplies theR component estimation value for the pixel-of-interest (x, y) estimatedin the above-described step S32 or S33 by the R component correctioncoefficient of the pixel-of-interest (x, y) to calculate the correctionamount of the R component for the pixel value of the pixel-of-interest(x, y) (step S34). After step S34, the image processing apparatus 40returns to the subroutine of the correction amount calculationprocessing in FIG. 3.

G Component Correction Amount Calculation Processing

Next, details of the G component correction amount calculationprocessing described in step S14 of FIG. 3 will be described. FIG. 5 isa flowchart illustrating an outline of G component correction amountcalculation processing.

As illustrated in FIG. 5, in a case where the pixel-of-interest (x, y)is a G pixel (step S41: Yes), the correction amount calculation unit 441first executes G component estimation value calculation processing basedon average (step S42), that is, processing of calculating an estimationvalue of the G component based on the average value of the pixel valueof the pixels surrounding the pixel-of-interest (x, y). Details of the Gcomponent estimation value calculation processing based on average willbe described below. After step S42, the image processing apparatus 40proceeds to step S44.

In step S41, in a case where the pixel-of-interest (x, y) is not a Gpixel (step S41: No), the correction amount calculation unit 441executes G component estimation value calculation processing based on asimilarity level (step S43), that is, processing of calculating theestimation value of the G component based on the similarity level of thepixel value of the pixel-of-interest (x, y). Details of the G componentestimation value calculation processing based on a similarity level willbe described below. After step S43, the image processing apparatus 40proceeds to step S44.

Subsequently, the correction amount calculation unit 441 multiplies theG component estimation value for the pixel-of-interest (x, y) estimatedin the above-described step S42 or S43 by the G component correctioncoefficient of the pixel-of-interest (x, y) to calculate the correctionamount of the G component for the pixel value of the pixel-of-interest(x, y) (step S44). After step S44, the image processing apparatus 40returns to the subroutine of the correction amount calculationprocessing in FIG. 3.

B Component Correction Amount Calculation Processing

Next, details of the B component correction amount calculationprocessing described in step S15 of FIG. 3 will be described. FIG. 6 isa flowchart illustrating an outline of B component correction amountcalculation processing.

As illustrated in FIG. 6, in a case where the pixel-of-interest (x, y)is a B pixel (step S51: Yes), the correction amount calculation unit 441first executes B component estimation value calculation processing basedon average (step S52), that is, processing of calculating an estimationvalue of the B component based on the average value of the pixel valueof the pixels surrounding the pixel-of-interest (x, y). Details of the Bcomponent estimation value calculation processing based on average willbe described below. After step S52, the image processing apparatus 40proceeds to step S54.

In step S51, in a case where the pixel-of-interest (x, y) is not a Bpixel (step S51: No), the correction amount calculation unit 441executes B component estimation value calculation processing based on asimilarity level (step S53), that is, processing of calculating theestimation value of the B component based on the similarity level of thepixel value of the pixel-of-interest (x, y). Details of the B componentestimation value calculation processing based on a similarity level willbe described below. After step S53, the image processing apparatus 40proceeds to step S54.

Subsequently, the correction amount calculation unit 441 multiplies theB component estimation value for the pixel-of-interest (x, y) estimatedin the above-described step S52 or S53 by the B component correctioncoefficient of the pixel-of-interest (x, y) to calculate the correctionamount of the B component for the pixel value of the pixel-of-interest(x, y) (step S54). After step S54, the image processing apparatus 40returns to the subroutine of the correction amount calculationprocessing in FIG. 3.

While the correction amount calculation unit 441 calculates thecorrection amount of all the components for all the pixels as describedabove with reference to FIGS. 3 to 6, it is also allowable to calculatethe correction amount for the pixel and a color component of a specificcolor.

R Component Estimation Value Calculation Processing Based on Average

Next, details of the R component estimation value calculation processingbased on average described in step S32 of FIG. 4 will be described. FIG.7 is a flowchart illustrating an outline of the R component estimationvalue calculation processing based on average.

As illustrated in FIG. 7, the correction amount calculation unit 441first calculates an average value AveR of pixel values of R pixelssurrounding the pixel-of-interest (x, y) (step S61). Herein,“surrounding” refers to a range of M×N pixels (M and N are odd numbersof 3 or more, except that M=N=3 is excluded for the R component and theB-component) with respect to the pixel-of-interest (x, y) as a center.Note that the correction amount calculation unit 441 may calculate theaverage value AveR including the pixel value of the pixel-of-interest(x, y). Alternatively, the correction amount calculation unit 441 maycalculate a statistical value in addition to the average value. Forexample, the correction amount calculation unit 441 may calculate any ofa weighted average value, a median value, and the above-mentionedstatistical values excluding the correction coefficient whose absolutevalue is a fixed value or above.

Subsequently, the correction amount calculation unit 441 calculates theaverage value AveR calculated in the above-described step S61 as an Rcomponent estimation value of the pixel-of-interest (x, y) (step S62).After step S62, the image processing apparatus 40 returns to the Rcomponent correction amount calculation processing in FIG. 4. In thefirst embodiment, the G component estimation value calculationprocessing based on average in step S42 of FIG. 5 and the B componentestimation value calculation processing based on average in step S52 ofFIG. 6 may be used to respectively calculate the G component estimationvalue or the B component estimation value by substituting the Rcomponent with the G component and B component and by performing similarprocessing to the processing described above. Accordingly, descriptionof the G component estimation value calculation processing based onaverage and the B component estimation value calculation processingbased on average will be omitted.

R Component Estimation Value Calculation Processing by Similarity Level

Next, details of the R component estimation value calculation processingbased on a similarity level described in step S33 of FIG. 4 will bedescribed. FIG. 8 is a flowchart illustrating an outline of the Rcomponent estimation value calculation processing based on a similaritylevel. FIG. 9 is a diagram schematically illustrating a method ofcalculating the R component estimation value to be calculated by thecorrection amount calculation unit 441.

As illustrated in FIG. 8, the correction amount calculation unit 441calculates an average value AveCoef of the R component correctioncoefficient of the pixels of a same color as the pixel-of-interest (x,y), surrounding the pixel-of-interest (x, y) (step S71). In thefollowing description, the pixel of a same color as thepixel-of-interest (x, y), surrounding the pixel-of-interest (x, y) willbe referred to as a reference pixel. The term surrounding means a rangesimilar to the range in step S61 in FIG. 7 described above. In the firstembodiment, the reference pixel functions as the surrounding pixel of asame color.

Subsequently, the correction amount calculation unit 441 calculates anaverage value AvePix of pixel values of the pixels of a same color asthe pixel-of-interest (x, y), surrounding the pixel-of-interest (x, y)(step S72). In this case, the correction amount calculation unit 441calculates the average value AvePix from the pixel values of theabove-described reference pixels.

Thereafter, the correction amount calculation unit 441 initializes acandidate value Est (Est=Est 0) (step S73). In this case, the correctionamount calculation unit 441 desirably sets a maximum possible value Est0 for a pixel value input to the spectral sensitivity variationcorrection unit 44 as the candidate value Est.

Subsequently, the correction amount calculation unit 441 initializes asimilarity level Sim (Sim=MAX) (step S74). In this case, the correctionamount calculation unit 441 sets a maximum possible value for thesimilarity level Sim. The similarity level Sim is defined such that themore similar (the higher the similarity level), the smaller the value,that is, the less similar (the lower the similarity level), the largerthe value.

Thereafter, the correction amount calculation unit 441 initializes acounter Step to zero (Step=0) (step S75).

Subsequently, the correction amount calculation unit 441 calculates twolarge and small candidate values Estp and Estm (step S76). Specifically,as illustrated in FIG. 9, the correction amount calculation unit 441calculates the two large and small candidate values Estp and Estm forthe candidate value Est by the following Formulas (1) and (2).Estp=Est+(Est0/2^(Step))  (1)Estm=Est−(Est0/2^(Step))  (2)

Thereafter, the correction amount calculation unit 441 executes thesimilarity level calculation processing for each of R componentcandidate values, that is, processing of calculating the similaritylevel in the estimation value of each of R components (step S77).

Similarity Level Calculation Processing for Each of R ComponentEstimation Values

FIG. 10 is a flowchart illustrating an outline of the similarity levelcalculation processing for each of R component candidate valuesdescribed in step S77 of FIG. 8.

As illustrated in FIG. 10, the correction amount calculation unit 441first obtains a pixel value and an R component correction coefficient ofthe pixel of a same color as the pixel-of-interest (x, y), surroundingthe pixel-of-interest (x, y) from the second buffer unit 422 (step S91).Specifically, the correction amount calculation unit 441 obtains a pixelvalue and an R component correction coefficient of the reference pixelfrom the second buffer unit 422.

Subsequently, the correction amount calculation unit 441 calculates atheoretical value Ideal for each of the pixels (step S92). Specifically,for the reference pixel (x+Δx, y+Δy) obtained in the above-describedstep S91, the correction amount calculation unit 441 calculates a valueobtained by multiplying a difference between the R component correctioncoefficient CoefR (x+Δx, y+Δy) and the average value AveCoef of the Rcomponent correction coefficient calculated in step S71 of FIG. 8 by theR component candidate value Est, as a theoretical value Ideal (x+Δx,y+Δy), for each of the pixels. More specifically, the correction amountcalculation unit 441 calculates the theoretical value Ideal (x+Δx, y+Δy)for each of the pixels by the following Formula (3).Ideal(x+Δx,y+Δy)=(CoefR(x+Δx,y+Δy)−AveCoef)×Est  (3)

Thereafter, the correction amount calculation unit 441 calculates ameasured value Val for each of the pixels (step S93). Specifically, forthe reference pixel (x+Δx, y+Δy), the correction amount calculation unit441 calculates a difference between a pixel value Pix (x+Δx, y+Δy) andthe average value AvePix of the pixel value calculated in step S72 ofFIG. 8, as the measured value Val (x+Δx, y+Δy) for each of the pixels.More specifically, the correction amount calculation unit 441 calculatesthe measured value Val (x+Δx, y+Δy) for each of the pixels by thefollowing Formula (4).Val(x+Δx,y+Δy)=Pix(x+Δx,y+Δy)−AvePix   (4)

Subsequently, the correction amount calculation unit 441 calculates anabsolute difference value Diff between the theoretical value Ideal andthe measured value Val for each of the pixels (step S94). Specifically,the correction amount calculation unit 441 calculates, for the pixel(x+Δx, y+Δy), the absolute difference value Diff (x+Δx, y+Δy) betweenthe theoretical value Ideal (x+Δx, y+Δy) and the measured value Val(x+Δx, y+Δy) for each of the pixels. More specifically, the correctionamount calculation unit 441 calculates the absolute difference valueDiff (x+Δx, y+Δy) for each of the pixels by the following Formula (5).Diff(x+Δx,y+Δy)=|(Val(x+Δx,y+Δy)−Ideal(x+Δx,y+Δy)|  (5)

Alternatively, the correction amount calculation unit 441 may calculatea squared difference value as Diff (x+Δx, y+Δy) by the following Formula(6), instead of calculating the absolute difference value Diff (x+Δx,y+Δy).Diff(x+Δx,y+Δy)=((Val(x+Δx,y+Δy)−Ideal(x+Δx,y+Δy))²  (6)

Thereafter, the correction amount calculation unit 441 calculates a sumvalue of the absolute difference values Diff of all the reference pixelsas the similarity level Sim (step S95). Alternatively, the correctionamount calculation unit 441 may calculate a weighted sum instead of asimple sum of the absolute difference values of all the referencepixels. For example, in a case of performing weighting, the correctionamount calculation unit 441 may calculate the sum of the absolutedifference values of all the reference pixels while changing theweighting coefficient in accordance with a distance of each of thepixels from the pixel-of-interest (x, y). Moreover, in a case whereweighting is performed, the correction amount calculation unit 441 maycalculate the sum of the absolute difference values of all the referencepixels while changing the weighting coefficients such that the largerthe absolute difference value Diff, the larger the weighting (equivalentto transforming the absolute difference value Diff with a monotonicallyincreasing function to calculate the sum of absolute difference values).After step S95, the image processing apparatus 40 returns to the Rcomponent estimation value calculation processing based on a similaritylevel in FIG. 8.

In this manner, in the similarity level calculation processing for eachof R component estimation values, the correction amount calculation unit441 calculates the similarity degree between the pixel valuedistribution and the theoretical value distribution by the sum of theabsolute difference values or the sum of the squared difference valuesand defines this as the evaluation value. The estimation value, however,may be calculated by another method. For example, the correction amountcalculation unit 441 may calculate the evaluation value using normalizedcross-correlation (NCC), zero-mean normalized cross-correlation (ZNCC)or the like. Note that regarding the G component and the B component, bysubstituting the R component with the G component or the B component andperforming similar processing as above, it is possible to calculate thesimilarity level in the estimation value of each of the G components andthe similarity level in the estimation value of each of the Bcomponents. Accordingly, description of similarity level calculationprocessing in each of the G component estimation values and similaritylevel calculation processing in each of the B component estimationvalues will be omitted. This processing is performed for each of thecandidate values Est, Estp, Estm so as to calculate three similaritylevels Sim, Simp, and Simm, respectively.

Returning to FIG. 8, step S78 and subsequent processing will bedescribed.

In step S78, the correction amount calculation unit 441 selects acandidate value having the minimum similarity level. Specifically, thecorrection amount calculation unit 441 calculates (selects) candidatevalues (any of Est, Estp, and Estm) corresponding to the minimumsimilarity level among the three similarity levels Sim, Simp, and Simm.Specifically, as illustrated in FIG. 9, the correction amountcalculation unit 441 selects a candidate value at a minimum incomparison with three similarity levels, namely, two (large and small)similarity levels and the current similarity level, and repeats thisprocessing to search a candidate value with the minimum similaritylevel. After step S78, the image processing apparatus 40 proceeds tostep S79.

In step S79, the correction amount calculation unit 441 updates thecandidate value calculated in step S78 to the candidate value Est. Forexample, in a case where the candidate value calculated in step S78 isEstp, the correction amount calculation unit 441 sets Est to Estp(Est=Estp).

Thereafter, the correction amount calculation unit 441 increments n(n=n+1) (step S80).

Subsequently, in a case where the counter Step is smaller than n(Step<n) (step S81: Yes), the image processing apparatus 40 returns tothe above-described step S76. In contrast, in a case where the counterStep is not smaller than n (step S81: No), the image processingapparatus 40 proceeds to step S82. Here, n is desirably a value suchthat Est 0÷2^(n) is 1 or more.

In step S82, the correction amount calculation unit 441 calculates thecandidate value Est as an R component estimation value. After step S82,the image processing apparatus 40 returns to the R component correctionamount calculation processing in FIG. 4.

In this manner, the R component estimation value calculation processingbased on a similarity level as described above does not use the pixelvalues of different-color pixels surrounding the pixel-of-interest, andthus, the correction amount calculation unit 441 may calculate anestimation value of a color component at the pixel-of-interest,different from the color component of the pixel-of-interest, even when apixel of a different color is saturated.

Specifically, according to the first embodiment, the correction amountcalculation unit 441 uses R component estimation value calculationprocessing to select a candidate value at a minimum in comparison withthree similarity levels, namely, two (large and small) similarity levelsand the current similarity level, and repeats this processing to searcha candidate value with the minimum similarity level. With thisconfiguration, the correction amount calculation unit 441 does not usethe pixel values of different-color pixels surrounding thepixel-of-interest, making it possible to calculate an estimation valueof a color component at the pixel-of-interest, different from the colorcomponent of the pixel-of-interest, even when a pixel of a differentcolor is saturated. Note that the correction amount calculation unit 441may search a candidate value having the highest similarity level byanother method. For example, the correction amount calculation unit 441may use a method of comprehensively arrange a candidate value to searchfor a value with the highest similarity level, a known hill climbingmethod, a local search method, or the like. In the first embodiment, theG component estimation value calculation processing by similarity levelin step S43 of FIG. 5 and the B component estimation value calculationprocessing by similarity level in step S53 of FIG. 6 may be used torespectively calculate the G component estimation value or the Bcomponent estimation value by substituting the R component with the Gcomponent and B component and by performing similar processing to theprocessing described above. Accordingly, description of the G componentestimation value calculation processing based on a similarity level andthe B component estimation value calculation processing based on asimilarity level will be omitted.

Correction Amount Calculation Processing for Pixel Value ofPixel-of-Interest (x, y)

Next, the correction amount calculation processing for the pixel valueof the pixel-of-interest (x, y) described in step S16 in FIG. 3 will bedescribed. FIG. 11 is a flowchart illustrating an outline of thecorrection amount calculation processing for the pixel value of thepixel-of-interest (x, y).

As illustrated in FIG. 11, the correction amount calculation unit 441first calculates a sum value Sumc of the correction amounts of the R, G,and B components calculated respectively in each of steps S13, S14, andS15 in FIG. 3 (step S101).

Subsequently, the correction amount calculation unit 441 calculates thesum value Sumc calculated in the above-described step S101 as acorrection amount of the pixel-of-interest (x, y) (step S102). Afterstep S102, the image processing apparatus 40 returns to the correctionamount calculation processing of FIG. 3.

The above-described first embodiment does not use the pixel values ofdifferent-color pixels surrounding the pixel-of-interest, and thus, thecorrection amount calculation unit 441 may calculate an estimation valueof a color component at the pixel-of-interest, different from the colorcomponent of the pixel-of-interest, even when a pixel of a differentcolor is saturated.

Modification of First Embodiment

Next, a modification of the first embodiment will be described. Amodification of the first embodiment has a similar configuration as theimaging system 1 according to the first embodiment described above, witha different manner of performing correction amount calculationprocessing on the pixel value of the pixel-of-interest (x, y) executedby the image processing apparatus. Specifically, the modification of thefirst embodiment is configured to prevent excessive correction anderroneous correction. Hereinafter, correction amount calculationprocessing for the pixel value of the pixel-of-interest (x, y) executedby the image processing apparatus according to the modification of thefirst embodiment will be described. A same reference sign will be givento the configuration identical to the configuration of the imagingsystem 1 according to the above-described first embodiment, anddescription for this will be omitted.

Correction Amount Calculation Processing for Pixel Value ofPixel-of-Interest (x, y)

FIG. 12 is a flowchart illustrating an outline of the correction amountcalculation processing for the pixel value of the pixel-of-interest (x,y) executed by the image processing apparatus according to themodification of the first embodiment.

As illustrated in FIG. 12, the correction amount calculation unit 441first calculates the sum value Sumc of the correction amounts of the R,G, and B components calculated respectively in each of steps S13, S14,and S15 in FIG. 3 (step S111).

Subsequently, the correction amount calculation unit 441 calculates anaverage value Ave of pixel values of the pixels of a same color as thepixel-of-interest (x, y), surrounding the pixel-of-interest (x, y) (stepS112). In this case, the correction amount calculation unit 441preferably uses the same pixel as the pixel of a same color for whichthe average value AvePix has been calculated in step S72 in FIG. 8.

Thereafter, the correction amount calculation unit 441 calculates adifference Delta (Delta=Pix−Ave) between the average value Avecalculated in step S112 and the pixel value Pix of the pixel-of-interest(x, y) (step S113).

Subsequently, in a case where the signs of Delta and Sumc are the same(step S114: Yes), and when |Delta|<|Sumc| is satisfied (step S115: Yes),the correction amount calculation unit 441 sets Sumc as Delta(Sumc=Delta) (step S116). After step S116, the image processingapparatus 40 proceeds to step S118 described below.

In step S114, in a case where the signs of Delta and Sumc are the same(step S114: Yes), and when |Delta|<|Sumc| is not satisfied (step S115:No), the image processing apparatus 40 proceeds to step S118 describedbelow.

In step S114, in a case where the signs of Delta and Sumc are not thesame (step S114: No), the correction amount calculation unit 441 setsSumc to “0” (Sumc=0) (step S117). After step S117, the image processingapparatus 40 proceeds to step S118 described below.

Subsequently, the correction amount calculation unit 441 calculates thesum value Sumc as the correction amount of the pixel-of-interest (x, y)(step S118). After step S118, the image processing apparatus 40 returnsto the correction amount calculation processing in FIG. 3.

According to the modification of the first embodiment described above,in a case where the signs of Delta and Sumc are not the same, thecorrection amount calculation unit 441 sets Sumc to “0”, and in a casewhere the signs of Delta and Sumc are the same and when |Delta|<|Sumc|is not satisfied, the correction amount calculation unit 441 does notperform correction, and this suppresses correction of the pixel value ofthe pixel-of-interest (x, y), making it possible to preventovercorrection and erroneous correction.

While the first embodiment performs comparison of the signs of Delta andSumc and determination of |Delta|<|Sumc| to limit the corrected pixelvalue to a range between Pix and Ave, it is allowable to use anothermethod. For example, the correction amount calculation unit 441 may clipthe value obtained by subtracting Sumc from Pix to a range from Ave toPix in a case where Ave<Pix is satisfied, while it may clip the valueobtained by subtracting Sumc from Pix to a range from Pix to Ave in acase where Ave≥Pix is satisfied.

Second Embodiment

Next, a second embodiment will be described. An imaging system accordingto the second embodiment has a same configuration as the imaging system1 according to the above-described first embodiment, and the imageprocessing apparatus executes R component correction amount calculationprocessing in a different manner as in the first embodiment.Specifically, while the above-described first embodiment separatelycalculate the estimation values of individual color components based onthe similarity level, the second embodiment separately calculates theestimation value of individual color components based on a ratio of thepixel value to the correction coefficient. Hereinafter, the R componentcorrection amount calculation processing executed by the imageprocessing apparatus according to the second embodiment will bedescribed. A same reference sign will be given to the configurationidentical to the configuration of the imaging system 1 according to theabove-described first embodiment, and description for this will beomitted. In the following description, the processing for the Rcomponent will be described, and description of the processing of the Gcomponent and the B component will be omitted since similar processingis performed also for the G component and the B component.

R Component Correction Amount Calculation Processing

FIG. 13 is a flowchart illustrating an outline of R component correctionamount calculation processing executed by the image processing apparatus40 according to the second embodiment. In FIG. 13, steps S121, S122, andS124 correspond to the above-described steps S31, S32, and S34 of FIG.4, respectively.

In step S123, the correction amount calculation unit 441 executes Rcomponent estimation value calculation processing based on the ratio ofthe pixel value to the correction coefficient, that is, processing ofcalculating the estimation value of the R component based on the ratioof the pixel value to the correction coefficient. After step S123, theimage processing apparatus 40 proceeds to step S124.

R Component Estimation Value Calculation Processing by Ratio of PixelValue to Correction Coefficient

FIG. 14 is a flowchart illustrating an outline of the R componentestimation value calculation processing based on the ratio of the pixelvalue to the correction coefficient described in step S123 of FIG. 13.Steps S201 and S202 in FIG. 14 correspond to steps S71 and S72 inabove-described FIG. 8, respectively.

In step S203, the correction amount calculation unit 441 calculates adifference ΔCoef of the correction coefficients from the average valuefor each of the pixels. Specifically, for the reference pixel (x+Δx,y+Δy), the correction amount calculation unit 441 calculates thedifference ΔCoef (x+Δx, y+Δy) between an R component correction amountCoefR (x+Δx, y+Δy) and an average value AveCoef of the R componentcorrection coefficient calculated in step S201 for each of the pixels.More specifically, the correction amount calculation unit 441 calculatesthe difference of the correction coefficient ΔCoef from the averagevalue for each of the pixels by the following Formula (7).ΔCoef(x+Δx,y+Δy)=CoefR(x+Δx,y+Δy)−AveCoef  (7)

Subsequently, the correction amount calculation unit 441 calculates adifference ΔVal of the pixel value from the average value for each ofthe pixels (step S204). Specifically, for the reference pixel (x+Δx,y+Δy), the correction amount calculation unit 441 calculates adifference ΔVal (x+Δx, y+Δy) between a pixel value Pix (x+Δx, y+Δy) andthe average value AvePix of the pixel value calculated in step S202 foreach of the pixels. More specifically, the correction amount calculationunit 441 calculates the difference ΔVal for each of the pixels by thefollowing Formula (8).ΔVal(x+Δx,y+Δy)=Pix(x+Δx,y+Δy)−AvePix  (8)

Thereafter, the correction amount calculation unit 441 calculates aratio Ratio of ΔVal to ΔCoef for each of the pixels (step S205).Specifically, for the reference pixel (x+Δx, y+Δy), the correctionamount calculation unit 441 calculates the ratio Ratio (x+Δx, y+Δy) ofΔVal (x+Δx, y+Δy) to ΔCoef (x+Δx, y+Δy) for each of the pixels. Morespecifically, the correction amount calculation unit 441 calculates theratio Ratio (x+Δx, y+Δy) for each of the pixels by the following Formula(9).Ratio(x+Δx,y+Δy)=ΔVal(x+Δx,y+Δy)/ΔCoef(x+Δx,y+Δy)  (9)

Note that in a case where ΔCoef (x+Δx, y+Δy) is zero, the correctionamount calculation unit 441 may perform processing of setting Ratio(x+Δx, y+Δy)=0 or processing of excluding processing of the pixel (x+Δx,y+Δy). At this time, the correction amount calculation unit 441 excludesthe excluded pixels (x+Δx, y+Δy) also in next average value calculationprocessing.

Subsequently, the correction amount calculation unit 441 calculates theaverage value of the ratio Ratio as the candidate value Est (step S206).Specifically, the correction amount calculation unit 441 calculates theaverage value of the ratio Ratio of all the reference pixels calculatedin step S205 as the candidate value Est. After step S206, the imageprocessing apparatus 40 returns to the R component correction amountcalculation processing in FIG. 4. In addition to the average value, thecorrection amount calculation unit 441 may use a statistical value suchas a weighted average value or a median value.

Moreover, the correction amount calculation unit 441 may calculate theaverage value of the ratio Ratio of the representative reference pixelsas the candidate value Est. Herein, the representative reference pixelis, for example, pixels up to a kth pixel from the largest absolutevalue of the correction coefficient, or the kth pixel from the largestabsolute value of the correction coefficient, or the like.

FIG. 15 is a diagram schematically illustrating distribution ofcorrection coefficients similar to each other. In FIG. 15, thehorizontal axis represents the correction coefficient and the verticalaxis represents the number of pixels. In FIG. 15, a curve L1 indicatesthe number of pixels with respect to the correction coefficient.

As illustrated in FIG. 15, in comparison of a case of selecting the kthcorrection coefficient with a small absolute value with a case ofselecting the kth correction coefficient with a large absolute value,the proportion of the k errors is small in the case of large absolutevalue of the correction coefficient, leading to higher accuracy of theestimation value obtained.

Furthermore, the correction amount calculation unit 441 may calculatethe average value of the ratio Ratio for each of reference pixels havingsimilar correction coefficient values, and thereafter may calculate theaverage value as the candidate value Est. This calculation makes itpossible to reduce the influence of variation in pixel value due torandom noise.

According to the second embodiment described above, the correctionamount calculation unit 441 calculates the candidate value Est of the Rcomponent based on the ratio of the pixel value to the correctioncoefficient, and this eliminates a need to search for an estimationvalue having minimum similarity level as in the above-described firstembodiment, making it possible to calculate the candidate value Est byone calculation procedure.

Third Embodiment

Next, a third embodiment will be described. Third embodiment has a sameconfiguration as the imaging system 1 according to the above-describedfirst embodiment, except that an image processing apparatus executescorrection amount calculation processing in a different manner as in thefirst embodiment. Specifically, while the above-described firstembodiment separately calculates the estimation value of each of the Rcomponent, the B component, and the G component, the third embodimentcollectively calculates estimation values of the R component, the Bcomponent, and the G component. Hereinafter, correction amountcalculation processing executed by the image processing apparatusaccording to the third embodiment will be described. A same referencesign will be given to the configuration identical to the configurationof the above-described first embodiment, and description thereof will beomitted.

Correction Amount Calculation Processing

FIG. 16 is a flowchart illustrating an outline of correction amountcalculation processing executed by the image processing apparatus 40according to the third embodiment. In FIG. 16, steps S301, S302, andS304 to S308 correspond to the above-described steps S11, S12, and S16to S20 of FIG. 3, respectively.

In step S303, the correction amount calculation unit 441 executesindividual color component correction amount calculation processing,that is processing of calculating the correction amount of individualcolor components in the pixel-of-interest (x, y).

Individual Color Component Correction Amount Calculation Processing

FIG. 17 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing in step S303 in FIG.16.

As illustrated in FIG. 17, the correction amount calculation unit 441first executes estimation value calculation processing based on averagefor a same color component as the pixel-of-interest (x, y) (step S311).Specifically, the correction amount calculation unit 441 performs, inaccordance with the color component of the pixel-of-interest (x, y), anyof: the R component estimation value calculation processing based onaverage in step S32 in the R component correction amount calculationprocessing of FIG. 4; the G component estimation value calculationprocessing based on average in step S42 in the G component correctionamount calculation processing of FIG. 5; and the B component estimationvalue calculation processing based on average in step S52 in the Bcomponent correction amount calculation processing of FIG. 6 describedabove.

Subsequently, the correction amount calculation unit 441 executesdifferent-color component estimation value calculation processing basedon a similarity level for different-color components of thepixel-of-interest (x, y) (step S312). For example, in a case where thepixel-of-interest (x, y) is a G pixel, the correction amount calculationunit 441 calculates estimation values of the R component and the Bcomponent. Details of the different-color component estimation valuecalculation processing for the different-color component of thepixel-of-interest (x, y) based on a similarity level will be describedbelow.

Thereafter, the correction amount calculation unit 441 calculates thecorrection amount of individual color components (step S313).Specifically, the correction amount calculation unit 441 multiplies theestimation value of individual color components of the pixel-of-interest(x, y) by the correction coefficient to calculate the correction amountof individual color components with respect to the pixel value of thepixel-of-interest (x, y). After step S313, the image processingapparatus 40 returns to the correction amount calculation processing ofFIG. 16.

Different-Color Component Estimation Value Calculation Processing Basedon a Similarity Level for Different-Color Component of Pixel-of-Interest(x, y)

Next, different-color component estimation value calculation processingbased on a similarity level for the different-color component of thepixel-of-interest (x, y) described in step S312 of FIG. 17 will bedescribed. FIG. 18 is a flowchart illustrating an outline ofdifferent-color component estimation value calculation processing basedon a similarity level for different color components of thepixel-of-interest (x, y).

As illustrated in FIG. 18, the correction amount calculation unit 441calculates an average value AveCoefC1 and an average value AveCoefC2 ofthe different-color component correction coefficients of the pixel of asame color as the pixel-of-interest (x, y), surrounding thepixel-of-interest (x, y) (Step S321). Specifically, the correctionamount calculation unit 441 defines the different-color pixels as the C1and C2 and defines the average values of the correction coefficients ofthe different-color pixels C1 and C2 as AveCoefC1 and AveCoefC2,respectively.

Subsequently, the correction amount calculation unit 441 calculates anaverage value AvePix of pixel values of the pixels of a same color asthe pixel-of-interest (x, y) surrounding the pixel-of-interest (x, y)(step S322). In this case, the correction amount calculation unit 441desirably calculates the average value AvePix of the pixel values fromthe pixel value of the reference pixel. At this time, the correctionamount calculation unit 441 may calculate a statistical value inaddition to the average value. For example, the correction amountcalculation unit 441 may calculate a weighted average value, a medianvalue, or the like.

Thereafter, the correction amount calculation unit 441 initializes acandidate value EstC1 and a candidate value EstC2 (EstC1=EstC2=0) (stepS323), and initializes a candidate value EstC1′ and a candidate valueEstC2′ (EstC1′=EstC2′=0) (step S324).

Subsequently, the correction amount calculation unit 441 initializes thesimilarity level Sim (Sim=MAX) (step S325) and initializes the candidatevalue EstC2′ (EstC2′=0) (step S326).

Thereafter, the correction amount calculation unit 441 executessimilarity level Sim′ calculation processing in the candidate valuesEstC1′ and EstC2′, that is, processing of calculating a similarity levelSim′ in the candidate value EstC1′ and the candidate value EstC2′ (stepS327).

Similarity Level Sim′ Calculation Processing for Candidate Values EstC1′and EstC2′

FIG. 19 is a flowchart illustrating an outline of the similarity levelSim′ calculation processing in the candidate values EstC1′ and EstC2′described in step S327 of FIG. 18.

As illustrated in FIG. 19, the correction amount calculation unit 441first outputs a pixel value and a different-color component correctioncoefficient of the pixel of a same color as the pixel-of-interest (x,y), surrounding the pixel-of-interest (x, y) from the second buffer unit422 (step S341).

Subsequently, the correction amount calculation unit 441 calculates thetheoretical value Ideal for each of the pixels (step S342).Specifically, the correction amount calculation unit 441 calculates, onthe reference pixel (x+Δx, y+Δy), the theoretical value Ideal by firstcalculating a difference between the correction coefficient CoefC1(x+Δx, y+Δy) of the C1 component and the correction coefficient CoefC2(x+Δx, y+Δy) of the C2 component, and the average values AveCoefC1 andAveCoefC2 of the different-color component correction coefficientscalculated in step S321 of FIG. 18 described above. The obtaineddifference is then respectively multiplied by the input different-colorcomponent candidate values EstC1′ and EstC2′, and the obtained valuesare added to each other to obtain the theoretical value Ideal (x+Δx,y+Δy). More specifically, the correction amount calculation unit 441calculates the theoretical value Ideal(x+Δx, y+Δy) for each of thepixels by the following Formula (10).

$\begin{matrix}{{{Ideal}\mspace{11mu}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} = {{\left( {{{CoefC}\; 1\mspace{11mu}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} - {{AveCoefC}\; 1}} \right) \times {EstC}\; 1^{\prime}} + {\left( {{{CoefC}\; 2\mspace{11mu}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} - {{AveCoefC}\; 2}} \right) \times {EstC}\; 2^{\prime}}}} & (10)\end{matrix}$

Thereafter, the correction amount calculation unit 441 calculates ameasured value Val for each of the pixels (step S343). Specifically, onthe reference pixel (x+Δx, y+Δy), the correction amount calculation unit441 calculates a difference between a pixel value Pix (x+Δx, y+Δy) andthe average value AvePix of the pixel value calculated in step S322 ofFIG. 18, as the measurement Val (x+Δx, y+Δy). More specifically, thecorrection amount calculation unit 441 calculates the measurement Val(x+Δx, y+Δy) for each of the pixels by the following Formula (11).Val(x+Δx,y+Δy)=Pix(x+Δx,y+Δy)−AvePix   (11)

Subsequently, the correction amount calculation unit 441 calculates anabsolute difference value Diff between the theoretical value Ideal andthe measured value Val for each of the pixels (step S344). Specifically,in the pixel (x+Δx, y+Δy), the correction amount calculation unit 441calculates the absolute difference value Diff (x+Δx, y+Δy) between thetheoretical value Ideal (x+Δx, y+Δy) calculated in the above-describedstep S342 and the measured value Val (x+Δx, y+Δy) calculated in theabove-described step S343 for each of the pixels. More specifically, thecorrection amount calculation unit 441 calculates the absolutedifference value Diff (x+Δx, y+Δy) for each of the pixels by theabove-described Formula (5). Alternatively, the correction amountcalculation unit 441 may calculate a squared difference value as Diff(x+Δx, y+Δy) by the above-described Formula (6) instead of the absolutedifference value Diff (x+Δx, y+Δy).

Subsequently, the correction amount calculation unit 441 calculates thesum value of the absolute difference values Diff as the similarity levelSim′ (step S345). Specifically, the correction amount calculation unit441 calculates the sum value of the absolute difference values of allthe reference pixels as the similarity level Sim′. Note that thecorrection amount calculation unit 441 may calculate the weighted sum asthe similarity level Sim′. In this case, the correction amountcalculation unit 441 may change the weighting coefficient in accordancewith a distance of each of the pixels from the pixel-of-interest (x, y).After step S345, the image processing apparatus 40 returns to thedifferent-color component estimation value calculation processing basedon a similarity level for the different-color component of thepixel-of-interest (x, y) in FIG. 18.

In this manner, in the similarity level Sim′ calculation processing foreach of candidate values EstC1′ and EstC2′, the correction amountcalculation unit 441 calculates the similarity degree between the pixelvalue distribution and the theoretical value distribution by the sum ofthe absolute difference value or the sum of the squared differencevalues and defines this as the evaluation value. The estimation value,however, may be calculated by another method. For example, thecorrection amount calculation unit 441 may calculate the evaluationvalue using normalized cross-correlation (NCC), zero-mean normalizedcross-correlation (ZNCC) or the like.

Returning to FIG. 18, step S328 and subsequent processing will bedescribed.

In a case where Sim′<Sim is satisfied (step S328: Yes) in step S328, thecorrection amount calculation unit 441 updates the candidate valueEstC1, the candidate value EstC2 and the similarity level Sim(EstC1=EstC1′, EstC2=EstC2′, Sim=Sim′) (step S329). After step S329, theimage processing apparatus 40 proceeds to step S330.

In step S328, in a case where Sim′<Sim is not satisfied (step S328: No),the image processing apparatus 40 proceeds to step S330.

Subsequently, the correction amount calculation unit 441 increments thecandidate value EstC2′ (step S330). In this case, the correction amountcalculation unit 441 increments the value by one or more.

Thereafter, in a case where EstC2′≤EstC2Max is satisfied (step S331:Yes), the image processing apparatus 40 returns to the above-describedstep S327. In contrast, in a case where EstC2′≤EstC2Max is not satisfied(step S331: No), the image processing apparatus 40 proceeds to stepS332.

In step S332, the correction amount calculation unit 441 increments thecandidate value EstC1′ (step S332). In this case, the correction amountcalculation unit 441 increments the value by one or more.

Thereafter, in a case where EstC1′≤EstC1Max is satisfied (step S333:Yes), the image processing apparatus 40 returns to the above-describedstep S326. In contrast, in a case where EstC1′≤EstC1Max is not satisfied(step S333: No), the image processing apparatus 40 proceeds to stepS334.

In step S334, the correction amount calculation unit 441 calculates thecandidate value EstC1 and the candidate value EstC2 as different-colorcomponent estimation values. After step S334, the image processingapparatus 40 returns to the individual color component correction amountcalculation processing in FIG. 17.

According to the third embodiment described above, since the correctionamount calculation unit 441 collectively calculates the estimationvalues of different-color components, it is possible to calculate anestimation value with high accuracy even in the case of the image dataobtained by capturing a subject of a color in which R component, Gcomponent, and B component are mixed.

Moreover, in the third embodiment, the correction amount calculationunit 441 may be configured to search the estimation value having theminimum similarity level by another method. For example, the correctionamount calculation unit 441 may use a method of comprehensively arrangean estimation value to search for the minimum value, a known hillclimbing method, a local search method, or the like.

Fourth Embodiment

Next, a fourth embodiment will be described. An imaging system accordingto the fourth embodiment has a same configuration as the imaging system1 according to the above-described third embodiment, except that theimage processing apparatus executes individual color componentcorrection amount calculation processing in a different manner as in thethird embodiment. Specifically, in the fourth embodiment, estimationvalues of all color components are collectively calculated. Hereinafter,individual color component correction amount calculation processingexecuted by the image processing apparatus according to the fourthembodiment will be described. A same reference sign will be given to theconfiguration identical to the configuration of the imaging system 1according to the above-described third embodiment, and description forthis will be omitted.

Outline of Individual Color Component Correction Amount CalculationProcessing

FIG. 20 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing executed by the imageprocessing apparatus 40 according to the fourth embodiment.

As illustrated in FIG. 20, the correction amount calculation unit 441first executes, for individual color components, individual colorcomponent estimation value calculation processing based on a similaritylevel, that is, processing of calculating estimation values ofindividual color components including a self color component and all thedifferent-color components (step S401).

Individual Color Component Estimation Value Calculation Processing Basedon a Similarity Level for Individual Color Components

FIG. 21 is a flowchart illustrating an outline of individual colorcomponent estimation value calculation processing based on a similaritylevel for individual color components.

As illustrated in FIG. 21, the correction amount calculation unit 441calculates an average value AveCoefR, an average value AveCoefG, anaverage value AveCoefB, which are average values of correctioncoefficients of individual color components of the pixel of a same coloras the pixel-of-interest (x, y), surrounding the pixel-of-interest (x,y) (step S411).

Subsequently, the correction amount calculation unit 441 calculates anaverage value AvePix of pixel values of pixels of a same color as thepixel-of-interest (x, y) surrounding the pixel-of-interest (x, y) (stepS412). In this case, the correction amount calculation unit 441desirably calculates the average value AvePix of the pixel values fromthe pixel value of the reference pixel.

Thereafter, the correction amount calculation unit 441 initializes thecandidate value EstR, the candidate value EstG and the candidate valueEstB (EstR=EstG=EstB=0) (step S413), and then, initializes the candidatevalue EstR′, the candidate value EstG′, and the candidate value EstB′(EstR′=EstG′=EstB′=0) (step S414).

Subsequently, the correction amount calculation unit 441 initializes thesimilarity level Sim (Sim=MAX) (step S415), initializes the candidatevalue EstG′ (EstG′=0) (step S416), and initializes the candidate valueEstB′ (EstB′=0) (step S417).

Thereafter, the correction amount calculation unit 441 executes thesimilarity level Sim′ calculation processing for the candidate valuesEstR′, EstG′, and EstB′, that is, processing of calculating thesimilarity level Sim′ for each of the candidate value EstR′, thecandidate value EstG′ and the candidate value EstB′ (Step S418).

Similarity Level Sim′ Calculation Processing in Candidate Values EstR′,EstG′, and EstB′

FIG. 22 is a flowchart illustrating an outline of the similarity levelSim′ calculation processing in the candidate values EstR′, EstG′, andEstB′ in step S418 of FIG. 21.

As illustrated in FIG. 22, the correction amount calculation unit 441first obtains a pixel value of a pixel of a same color as thepixel-of-interest (x, y), surrounding the pixel-of-interest (x, y) andindividual color component correction coefficients from the secondbuffer unit 422 (step S431). Specifically, the correction amountcalculation unit 441 obtains pixel values and correction coefficients ofthe reference pixel from the second buffer unit 422.

Subsequently, the correction amount calculation unit 441 calculates thetheoretical value Ideal for each of the pixels (step S432).Specifically, the correction amount calculation unit 441 calculates, onthe reference pixel (x+Δx, y+Δy), the theoretical value Ideal by firstcalculating a difference between the individual color componentcorrection coefficients CoefR (x+Δx, y+Δy), CoefG (x+Δx, y+Δy), andCoefB (x+Δx, y+Δy), and the average values AveCoefR, AveCoefG, andAveCoefB of the individual color component correction coefficientscalculated in step S411 of FIG. 21 described above. The obtaineddifference is then respectively multiplied by the input individual colorcomponent candidate values EstR′, EstG′, and EstB′, and the obtainedvalues are added to each other to obtain the theoretical value Ideal(x+Δx, y+Δy). More specifically, the correction amount calculation unit441 calculates the theoretical value Ideal(x+Δx, y+Δy) for each of thepixels by the following Formula (12).

$\begin{matrix}{{{Ideal}\;\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} = {{\left( {{{CoefR}\mspace{11mu}\left( {{x + {\Delta\; x}},\;{y + {\Delta\; y}}} \right)} - {AveCoefR}} \right) \times {EstR}^{\prime}} + {\left( {{{CoefG}\mspace{11mu}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} - {AveCoefG}} \right) \times {EstG}^{\prime}} + {\left( {{{CoefB}\mspace{11mu}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} - {AveCoefB}} \right) \times {EstB}^{\prime}}}} & (12)\end{matrix}$

Thereafter, the correction amount calculation unit 441 calculates themeasured value Val for each of the pixels (step S433). Specifically, onthe reference pixel (x+Δx, y+Δy), the correction amount calculation unit441 calculates a difference between a pixel value Pix (x+Δx, y+Δy) andthe average value AvePix of the pixel value calculated in step S412 ofFIG. 21, as the measurement Val (x+Δx, y+Δy). More specifically, thecorrection amount calculation unit 441 calculates the measured value Val(x+Δx, y+Δy) for each of the pixels by the following Formula (13).Val(x+Δx,y+Δy)=Pix(x+Δx,y+Δy)−AvePix   (13)

Subsequently, the correction amount calculation unit 441 calculates anabsolute difference value Diff between the theoretical value Ideal andthe measured value Val for each of the pixels (step S434). Specifically,the correction amount calculation unit 441 calculates the absolutedifference value Diff(x+Δx, y+Δy) between the theoretical valueIdeal(x+Δx, y+Δy) and the measured value Val(x+Δx, y+Δy) in the pixel(x+Δx, y+Δy) for each of the reference pixels. More specifically, thecorrection amount calculation unit 441 calculates the absolutedifference value Diff (x+Δx, y+Δy) for each of the pixels by thefollowing Formula (14).Diff(x+Δx,y+Δy)=|Val(x+Δx,y+Δy)−Ideal(x+Δx,y+Δy)|  (14)

Note that the correction amount calculation unit 441 may set the squareddifference value like Formula (15) as Diff (x+Δx, y+Δy) instead of theabsolute difference value Diff (x+Δx, y+Δy).Diff(x+Δx,y+Δy)=(Val(x+Δx,y+Δy)−Ideal(x+Δx,y+Δy))²  (15)

Thereafter, the correction amount calculation unit 441 calculates thesum value of the absolute difference values Diff as the similarity levelSim′ (step S435). Specifically, the correction amount calculation unit441 calculates the sum value of the absolute difference values of allthe reference pixels as the similarity level Sim′. Note that thecorrection amount calculation unit 441 may calculate the weighted sum asthe similarity level Sim′. In this case, the correction amountcalculation unit 441 may change the weighting coefficient in accordancewith a distance of each of the pixels from the pixel-of-interest (x, y).After step S435, the image processing apparatus 40 returns to theprocessing of individual color component estimation value calculationprocessing based on a similarity level for individual color componentsof FIG. 21.

In this manner, in the similarity level Sim′ calculation processing foreach of candidate values EstR′, EstG′, and EstB′, the correction amountcalculation unit 441 calculates the similarity degree between the pixelvalue distribution and the theoretical value distribution by the sum ofthe absolute difference value or the sum of the squared differencevalues and defines this as the evaluation value. The evaluation value,however, may be calculated by another method. For example, thecorrection amount calculation unit 441 may calculate the evaluationvalue using normalized cross-correlation (NCC), zero-mean normalizedcross-correlation (ZNCC) or the like.

Returning to FIG. 21, step S419 and subsequent processing will bedescribed.

In a case where Sim′<Sim is satisfied (step S419: Yes) in step S419, thecorrection amount calculation unit 441 updates the candidate valuesEstR, EstG, EstB (EstR=EstR′, EstG=EstG′, and EstB=EstB′) (Step S420).After step S420, the image processing apparatus 40 proceeds to stepS421.

In step S419, in a case where Sim′<Sim is not satisfied (step S419: No),the image processing apparatus 40 proceeds to step S421.

Subsequently, the correction amount calculation unit 441 increments thecandidate value EstB′ (step S421). In this case, the correction amountcalculation unit 441 increments the value by one or more.

Thereafter, in a case where the candidate value EstB′≤EstBMax issatisfied (EstBMax is a preset maximum value within a candidate valuesearch range of B) (step S422: Yes), the image processing apparatus 40returns to the above-described step S418. In contrast, in a case wherethe candidate value EstB′≤EstBMax is not satisfied (step S422: No), theimage processing apparatus 40 proceeds to step S423.

In step S423, the correction amount calculation unit 441 increments thecandidate value EstG′. In this case, the correction amount calculationunit 441 increments the value by one or more.

Subsequently, in a case where the candidate value EstG′≤EstGMax issatisfied (EstGMax is a preset maximum value of a candidate value searchrange of G) (step S424: Yes), the image processing apparatus 40 returnsto the above-described step S417. In contrast, in a case where thecandidate value EstB′≤EstGMax is not satisfied (step S424: No), theimage processing apparatus 40 proceeds to step S425.

In step S425, the correction amount calculation unit 441 increments thecandidate value EstR′. In this case, the correction amount calculationunit 441 increments the value by one or more.

Subsequently, in a case where the candidate value EstR′≤EstRMax issatisfied (EstRMax is the maximum value of a preset candidate valuesearch range of R) (step S426: Yes), the image processing apparatus 40returns to the above-described step S416. In contrast, in a case wherethe candidate value EstR′≤EstRMax is not satisfied (step S426: No), theimage processing apparatus 40 proceeds to step S427.

In step S427, the correction amount calculation unit 441 calculates thecandidate values EstR, EstG, and EstB as the individual color componentestimation values. After step S427, the processing returns to theindividual color component correction amount calculation processing inFIG. 20.

Returning to FIG. 20, step S402 and subsequent processing will bedescribed.

In step S402, the correction amount calculation unit 441 multiplies theestimation value of individual color components of the pixel-of-interest(x, y) calculated in step S401 described above by the correctioncoefficient to calculate the correction amount of individual colorcomponents for the pixel value of the pixel-of-interest (x, y). Afterstep S402, the image processing apparatus 40 returns to the individualcolor component correction amount calculation processing of FIG. 17.

According to the fourth embodiment described above, since the correctionamount calculation unit 441 collectively calculates the estimationvalues of all color components, it is possible to calculate anestimation value even in the case of the image data obtained bycapturing a subject of a color in which R component, G component, and Bcomponent are mixed.

Moreover, in the fourth embodiment, the correction amount calculationunit 441 may be configured to search the estimation value having theminimum similarity level by another method. For example, the correctionamount calculation unit 441 may use a method of comprehensively arrangean estimation value to search for the minimum value, a known hillclimbing method, a local search method, or the like.

Fifth Embodiment

Next, a fifth embodiment will be described. An imaging system accordingto the fifth embodiment has a same configuration as the imaging system 1according to the above-described third embodiment, and the imageprocessing apparatus execute individual color component correctionamount calculation processing in a different manner as in the thirdembodiment. Specifically, the fifth embodiment calculates the estimationvalue by solving simultaneous equations including a relationalexpression between a pixel value in a surrounding pixel, a correctioncoefficient of the reference pixel, and an estimation value, establishedfor each of the pixels. Hereinafter, individual color componentcorrection amount calculation processing executed by the imageprocessing apparatus according to the fifth embodiment will bedescribed. A same reference sign will be given to the configurationidentical to the configuration of the imaging system 1 according to theabove-described third embodiment, and description for this will beomitted.

Outline of Individual Color Component Correction Amount CalculationProcessing

FIG. 23 is a flowchart illustrating an outline of individual colorcomponent correction amount calculation processing executed by the imageprocessing apparatus 40 according to the fifth embodiment.

As illustrated in FIG. 23, the correction amount calculation unit 441first executes estimation value calculation processing based on averagefor a same color component as the pixel-of-interest (x, y) (step S441).

Subsequently, the correction amount calculation unit 441 executesdifferent-color component estimation value calculation processing basedon simultaneous equations, that is, processing of calculating anestimation value of a different-color component by simultaneousequations (step S442)

Different-Color Component Estimation Value Calculation Processing Basedon Simultaneous Equations

FIG. 24 is a flowchart illustrating an outline of different-colorcomponent estimation value calculation processing based on simultaneousequations in step S422 of FIG. 23.

As illustrated in FIG. 24, the correction amount calculation unit 441first calculates the average value AveCoefC1 and the average valueAveCoefC2 of the different-color component correction coefficients ofthe pixel of a same color as the pixel-of-interest (x, y), surroundingthe pixel-of-interest (x, y) (Step S451).

Subsequently, the correction amount calculation unit 441 calculates anaverage value AvePix of pixel values of the pixels of a same color asthe pixel-of-interest (x, y) surrounding the pixel-of-interest (x, y)(step S452). In this case, the correction amount calculation unit 441desirably calculates the average value AvePix of the pixel values fromthe pixel value of the reference pixel. At this time, the correctionamount calculation unit 441 may calculate a statistical value inaddition to the average value. For example, the correction amountcalculation unit 441 may calculate a weighted average value, a medianvalue, or the like.

Thereafter, the correction amount calculation unit 441 calculatesdifferences ΔCoefC1 and ΔCoefC2 from the average values of thecorrection coefficients for each of the pixels (step S453).Specifically, the correction amount calculation unit 441 calculates, onthe reference pixel (x+Δx, y+Δy), the differences ΔCoefC1 (x+Δx, y+Δy)and ΔCoefC2 (x+Δx, y+Δy) between the correction coefficient CoefC1(x+Δx, y+Δy) of the C1 component and the correction coefficient CoefC2(x+Δx, y+Δy) of the C2 component, and the average values AveCoefC1 andAveCoefC2 of the correction coefficients of the C1 and C2 components,calculated in step S451, for each of the pixels. Specifically, thecorrection amount calculation unit 441 calculates the difference ΔCoefC1(x+Δx, y+Δy) and ΔCoefC2 (x+Δx, y+Δy) of the correction coefficient foreach of the pixels from the average value by the following Formulas (16)and (17) for each of the pixels.ΔCoefC1(x+Δx,y+Δy)=CoefC1(x+Δx,y+Δy)−AveCoefC1  (16)ΔCoefC2(x+Δx,y+Δy)=CoefC2(x+Δx,y+Δy)−AveCoefC2  (17)

Subsequently, the correction amount calculation unit 441 calculates adifference ΔVal of the pixel value from the average value for each ofthe pixels (step S454). Specifically, on the reference pixel (x+Δx,y+Δy), the correction amount calculation unit 441 calculates adifference ΔVal (x+Δx, y+Δy) of the pixel values Pix (x+Δx, y+Δy) fromthe average value AvePix of the pixel values calculated in theabove-described step S452, for each of the pixels. More specifically,the correction amount calculation unit 441 calculates the differenceΔVal (x+Δx, y+Δy) by the following Formula (18).ΔVal(x+Δx,y+Δy)=Pix(x+Δx,y+Δy)−AvePix   (18)

Thereafter, the correction amount calculation unit 441 calculatescandidate values EstC1′ and EstC2′ from the simultaneous equations (stepS455). Specifically, the correction amount calculation unit 441 extractstwo pixels (x+Δx1, y+Δy1) and pixels (x+Δx2, y+Δy2) from the referencepixels so as to calculate candidate values EstC1′ and EstC2′ in theextracted pixels by solving the simultaneous equations (19) and (20) asfollows.ΔVal(x+Δx1,y+Δy1)=ΔCoefC1(x+Δx1,y+Δy1)×EstC1′+ΔCoefC2(x+Δx1,y+Δy1)×EstC2′  (19)ΔVa2(x+Δx2,y+Δy2)=ΔCoefC1(x+Δx2,y+Δy2)×EstC1′+ΔCoefC2(x+Δx2,y+Δy2)×EstC2′  (20)

The correction amount calculation unit 441 executes the above-describedcalculation on the two pixels extracted by the following method tocalculate the candidate values EstC1′ and EstC2′ for each of the pixels.Specifically, the correction amount calculation unit 441 executescalculation in all combinations of extracting two pixels from thereference pixel.

Moreover, the correction amount calculation unit 441 may performcalculation in all combinations of extracting two pixels fromrepresentative reference pixels. For example, the correction amountcalculation unit 441 defines k pixels counting from the pixel having thelargest absolute value of the correction coefficient as therepresentatives. In this case, in comparison of a case where thecorrection amount calculation unit 441 selects k pixels having smallabsolute value of the correction coefficient with a case where the unitselects k pixels having large absolute value of the correctioncoefficient, the proportion of the k errors is small in the case oflarge absolute value of the correction coefficient, leading to higheraccuracy of the estimation value obtained.

Furthermore, the correction amount calculation unit 441 may execute thecalculation for all cases in which two pixels are extracted from thepixels having similar correction coefficient values.

In step S456, the correction amount calculation unit 441 uses theplurality of candidate values EstC1′ and EstC2′ calculated in step S455to obtain their average values to calculate as the estimation valuesEstC1 and EstC2. Note that the correction amount calculation unit 441may calculate the values calculated from other statistical values suchas a weighted average value and a median value other than the averagevalue, as the estimation values EstC1 and EstC2. Moreover, thecorrection amount calculation unit 441 may calculate the value obtainedby calculating the average value of the candidate value by extraction oftwo pixels having the similar number of correction coefficients, as theestimation value Est. This calculation makes it possible to reduce theinfluence of variation in pixel value due to random noise.

Returning to FIG. 23, step S443 and subsequent processing will bedescribed.

In step S443, the correction amount calculation unit 441 multiplies theestimation value of individual color components of the pixel-of-interest(x, y) calculated in step S442 described above by the correctioncoefficient to calculate the correction amount of individual colorcomponents for the pixel value of the pixel-of-interest (x, y). Afterstep S443, the image processing apparatus 40 returns to the individualcolor component correction amount calculation processing in FIG. 17.

According to the fifth embodiment described above, the correction amountcalculation unit 441 may calculate the estimation value by solvingsimultaneous equations including a relational expression between thepixel value in the surrounding pixel of a same color, the correctioncoefficient of a surrounding pixel of a same color, and the estimationvalue, established for each of the pixels.

While the fifth embodiment is a case of calculating the estimation valueby solving simultaneous equations of two components, it is allowable tocalculate the estimation value by solving simultaneous equations ofthree components of R component, G component, and B component.

Sixth Embodiment

Next, a sixth embodiment will be described. An imaging system accordingto the sixth embodiment has a same configuration as the imaging system 1according to the above-described third embodiment, except that the imageprocessing apparatus executes individual color component correctionamount calculation processing in a different manner as in the thirdembodiment. Hereinafter, individual color component correction amountcalculation processing executed by the image processing apparatusaccording to the sixth embodiment will be described.

Individual Color Component Correction Amount Calculation Processing

FIG. 25 is a flowchart illustrating outline of individual colorcomponent correction amount calculation processing executed by the imageprocessing apparatus 40 according to the sixth embodiment. Steps S461and S463 in FIG. 25 correspond to steps S441 and S443 in above-describedFIG. 23, respectively.

In step S462, the correction amount calculation unit 441 executesdifferent-color component estimation value calculation processing basedon the ratio of the pixel value to the correction coefficient.

Different-Color Component Estimation Value Calculation Processing Basedon Ratio of Pixel Value to Correction Coefficient

FIG. 26 is a flowchart illustrating an outline of the different-colorcomponent estimation value calculation processing based on the ratio ofthe pixel value to the correction coefficient described in step S462 ofFIG. 25.

As illustrated in FIG. 26, the correction amount calculation unit 441first calculates the average value AveCoefC1 and the average valueAveCoefC2 of the different-color component correction coefficients ofthe pixel of a same color as the pixel-of-interest (x, y), surroundingthe pixel-of-interest (x, y) (step S471).

Subsequently, the correction amount calculation unit 441 calculates anaverage value AvePix of pixel values of the pixels of a same color asthe pixel-of-interest (x, y) surrounding the pixel-of-interest (x, y)(step S472). In this case, the correction amount calculation unit 441desirably calculates the average value AvePix of the pixel values fromthe pixel value of the reference pixel. At this time, the correctionamount calculation unit 441 may calculate a statistical value inaddition to the average value. For example, the correction amountcalculation unit 441 may calculate a weighted average value, a medianvalue, or the like.

Thereafter, the correction amount calculation unit 441 calculatesdifferences ΔCoefC1 and ΔCoefC2 from the average values of thecorrection coefficients in each of the pixels (step S473). Specifically,the correction amount calculation unit 441 calculates, on the referencepixel (x+Δx, y+Δy), the differences ΔCoefC1 and ΔCoefC2 between thecorrection coefficient CoefC1 (x+Δx, y+Δy) of the C1 component and thecorrection coefficient CoefC2 (x+Δx, y+Δy) of the C2 component, and theaverage values AveCoefC1 and AveCoefC2 of the different-color componentcorrection coefficients calculated in step S471, for each of the pixels.More specifically, the correction amount calculation unit 441 calculatesΔCoefC1 and ΔCoefC2 for each of the pixels by the following Formulas(21) and (22).ΔCoefC1(x+Δx,y+Δy)=CoefC1(x+Δx,y+Δy)−AveCoefC1  (21)ΔCoefC2(x+Δx,y+Δy)=CoefC2(x+Δx,y+Δy)−AveCoefC2  (22)

Subsequently, the correction amount calculation unit 441 calculates adifference ΔVal of the pixel value from the average value for each ofthe pixels (step S474). Specifically, on the reference pixel (x+Δx,y+Δy), the correction amount calculation unit 441 calculates adifference ΔVal (x+Δx, y+Δy) of the pixel values Pix (x+Δx, y+Δy) fromthe average value AvePix of the pixel values calculated in theabove-described step S472, for each of the pixels. More specifically,the correction amount calculation unit 441 calculates ΔVal (x+Δx, y+Δy)by the following Formula (23).ΔVal(x+Δx,y+Δy)=Pix(x+Δx,y+Δy)−AvePix  (23)

Thereafter, the correction amount calculation unit 441 calculates aratio ValC2/ValC1 of the pixel values of the pixel of a same color as C1and C2 in the vicinity of the pixel-of-interest (x, y) (step S475).Specifically, the correction amount calculation unit 441 defines thepixel values of the pixel having same colors as the C1 component and theC2 component in the vicinity of the pixel-of-interest (x, y) as ValC1and ValC2, and calculates the ratio ValC2/ValC1. The correction amountcalculation unit 441 may define the pixel value average values of thepixel of same colors as C1 and C2 within a M×N pixel range as ValC1 andValC2, with respect to the pixel-of-interest (x, y) as a center.Moreover, in a case where pixels in the vicinity are saturated, thecorrection amount calculation unit 441 may calculate the pixel valuesValC1 and ValC2 in a region of the pixel closest to thepixel-of-interest in which the ratio of the pixel values of C1 and C2may be calculated.

FIG. 27 is a diagram schematically illustrating a calculation region ofthe ratio (Ratio) in a case where a pixel of a different color in thevicinity of the pixel-of-interest (x, y) is saturated. In FIG. 27, thehorizontal axis represents the x-coordinate, and the vertical axisrepresents the pixel value. Moreover, in FIG. 27, a curve L10 indicatesa pixel value of the R pixel and a curve L11 indicates a pixel value ofthe B pixel. Moreover, in FIG. 27, a hatched region W1 indicates asaturated region.

As illustrated in FIG. 27, both the pixel C1 and the pixel C2 aresaturated in the hatched region W1 making it difficult to calculate theratio ValC2/ValC1 correctly. Therefore, the correction amountcalculation unit 441 calculates the pixel values ValC1 and ValC2 in theregion of the pixel closest to the pixel-of-interest in which the ratioof the pixel values of C1 and C2 may be calculated.

Returning to FIG. 26, step S476 and subsequent processing will bedescribed.

In step S476, the correction amount calculation unit 441 calculates thecandidate value EstC1′ for the C1 component for each of the pixels.Specifically, the correction amount calculation unit 441 calculates thecandidate value EstC1′ for each of the pixels by the following Formula(24).

$\begin{matrix}{{{EstC}\; 1^{\prime}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} = \frac{{\Delta{Val}}\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)}{{\Delta\;{CoefC}\; 1\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right)} + {\Delta\;{CoefC}\; 2{\left( {{x + {\Delta\; x}},{y + {\Delta\; y}}} \right) \cdot \frac{{ValC}\; 2}{{ValC}\; 1}}}}} & (24)\end{matrix}$

In a case where the denominator of the above equation is 0, thecorrection amount calculation unit 441 sets such that EstC1′ (x+Δx,y+Δy)=0, or excludes processing of the pixel (x+Δx, y+Δy). When pixelprocessing is excluded, the correction amount calculation unit 441excludes the same pixel in the next average value calculation processingas well, similarly to the current processing.

Thereafter, the correction amount calculation unit 441 calculates anaverage value of the candidate value EstC1′ as the estimation valueEstC1 (step S477). Specifically, the correction amount calculation unit441 calculates the average value of the candidate values EstC1′ of allthe pixels calculated in the above-described step S476, as theestimation value EstC1. In this case, the correction amount calculationunit 441 may calculate the values calculated from other statisticalvalues such as a weighted average value and a median value other thanthe average value, as the estimation value EstC1. It is assumed that thecorrection amount calculation unit 441 initializes EstC1 to 0 beforeexecuting the processing of step S477.

Subsequently, the correction amount calculation unit 441 calculates theestimation value EstC2 (step S478). Specifically, the correction amountcalculation unit 441 calculates the estimation value EstC2 using theestimation value EstC1 calculated in the above-described step S477 andthe ratio ValC2/ValC1 calculated in step S475. More specifically, thecorrection amount calculation unit 441 calculates the estimation valueEstC2 by the following Formula (25).EstC2=EstC1×(ValC2/ValC1)  (25)

After step S478, the image processing apparatus 40 returns to theindividual color component correction amount calculation processing ofFIG. 25.

According to the sixth embodiment described above, it is possible tocalculate the estimation value based on the ratio of the pixel value ofeach of the pixels in the surrounding pixels in which pixel values arenot saturated and the correction coefficient of each of the pixels inthe surrounding pixels of a same color.

While the sixth embodiment calculates the estimation value based on theratio of the pixel value of each of the pixels in the surrounding pixelsin which pixel values are not saturated and the correction coefficientof each of the pixels in the surrounding pixels of a same color, it isalso allowable to use this method to search for the minimum value of thesimilarity level in the above described first, third embodiment, or thelike.

Other Embodiments

While the color filter according to the embodiment is a Bayer arraycolor filter including an R filter, a G filter, and a B filter, thepresent disclosure may also be applied to other color filters. Forexample, the present disclosure is applicable to a color filterincluding the G filter, the B filter, and a complementary color Mg(magenta) filter to substitute the R filter in the color filteraccording to the above-described embodiment. Moreover, the presentdisclosure may be applicable to a color filter of a complementary colorfilter using a Cy (cyan) filter, a Mg filter and a Ye (yellow) filter.In the use of such a color filter, it would be sufficient to performprocessing by a known interpolation method of perform interpolation froma complementary color to a primary color.

Moreover, while the embodiment provide a plurality of color filtershaving different spectral transmittances on one image sensor, it is alsopossible to apply the present disclosure to a two-chip imaging apparatususing an image sensor exclusively including the G filter onlytransmitting the green wavelength region provided on a light receivingsurface of each of the pixels or all over the image sensor and using animage sensor including the R filters and the B filters respectivelytransmitting red or blue wavelength regions, being arranged alternatelyon the light receiving surface of each of the pixels so as to form acheckered pattern, or apply the present disclosure to a three-chipimaging apparatus using image sensors each of which includes the Rfilter alone, the G filter alone, and the B filter alone. In this case,in a case where the correction coefficient of the G pixel in one imagesensor is calculated, the correction amount according to the presentdisclosure may be calculated using the pixel value of the R pixel or theB pixel of the other image sensor corresponding to the coordinates ofthe G pixel.

According to the present disclosure, it is possible to correctvariations in spectral sensitivity of each of a plurality of pixelsconstituting the image sensor.

The present disclosure is not limited to the above-describedembodiments, but various modifications and further applications are ofcourse available within the scope. For example, it is allowable toinstall, as an image processing engine, the image processing apparatusaccording to the present disclosure to any apparatus capable of shootinga subject such as a mobile apparatus having an image sensor of a mobilephone or a smartphone or an imaging apparatus that images the subjectusing an optical device such as a video camera, an endoscope, asurveillance camera, or a microscope, besides the imaging system used inthe description.

Moreover, in the description of the flowcharts for the operationsdescribed above in the present specification, terms such as “first”,“next”, “subsequently”, and “thereafter” are used to describe operationfor convenience. These do not denote, however, that the operations needto be performed in this order.

Moreover, the methods of the processing performed by the imageprocessing apparatus in the above-described embodiments, that is, any ofthe processing illustrated in the flowcharts may be stored as a programthat may be executed by a control unit such as a CPU. In addition, it ispossible to distribute by storing in a storage medium of the externalstorage device, such as memory cards (ROM card, RAM card, etc.), amagnetic disk (flexible disk, hard disk, etc.), an optical disc (CD-ROM,DVD, etc.), and a semiconductor memory. The control unit such as a CPUreads the program stored in the storage medium of the external storagedevice and controls the operation by the read program to execute theabove-described processing.

Moreover, note that the present disclosure is not limited to theabove-described embodiments and modifications just as they are but maybe embodied by modifying the components without departing from the scopeof the disclosure at a stage of implementation of the disclosure.Furthermore, a plurality of components disclosed in the above-describedembodiments may be appropriately combined to form various disclosures.For example, some components may be omitted from the all the componentsdescribed in the embodiments and the modification example. Furthermore,the components described in each of exemplary embodiments andmodification examples may be appropriately combined with each other.

Moreover, a term which has been described at least once in thespecification or the drawings, associated with another term having abroader or similar meaning, may be substituted by this other termanywhere in the specification and the drawings. In this manner, variousmodifications and further application may be implemented within a scopethat does not depart from the present disclosure.

What is claimed is:
 1. An image processing apparatus comprising: aprocessor comprising hardware, wherein the processor is configured to:obtain image data generated by an image sensor that forms apredetermined array pattern using color filters of a plurality of colorshaving mutually different spectral transmittances and in which each ofthe color filters is arranged at a position corresponding to any of aplurality of pixels; obtain a correction coefficient for each of theplurality of pixels, the correction coefficient for correcting adifference between pixel values corresponding to a difference between aspectral sensitivity and a preset reference spectral sensitivity in apredetermined wavelength range in a pixel-of-interest; calculate anestimation value of a color component as a correction target in thepixel-of-interest using a pixel value of each of pixels in surroundingpixels of a same color and the correction coefficient, the surroundingpixel of a same color being a pixel surrounding the pixel-of-interestand being a color filter arranged on the each of the surrounding pixelsand a pixel including a color filter having a same color as a colorfilter arranged on the pixel-of-interest; calculate a correction amountof the pixel value of the pixel-of-interest based on the estimationvalue and the correction coefficient of the pixel-of-interest; andcorrect the pixel value of the pixel-of-interest based on the correctionamount calculated.
 2. The image processing apparatus according to claim1, wherein the processor is configured to: calculate a similarity levelfrom a theoretical value of the pixel value in the surrounding pixel ofa same color calculated based on a candidate value and the correctioncoefficient of each of the pixels in the surrounding pixel of a samecolor and from a measured value of the pixel value in the surroundingpixel of a same color; and calculate the candidate value having a highsimilarity level as the estimation value.
 3. The image processingapparatus according to claim 2, wherein the theoretical value is aproduct of the candidate value and the correction coefficient of each ofthe pixels in the surrounding pixels of a same color.
 4. The imageprocessing apparatus according to claim 2, wherein the similarity levelis a value based on a difference between the theoretical value and themeasured value of each of the pixels in the surrounding pixels of a samecolor.
 5. The image processing apparatus according to claim 1, whereinthe processor is configured to: divide a pixel value of each of thepixels in the surrounding pixels of a same color by the correctioncoefficient; and calculate the estimation value based on the valueobtained by the division.
 6. The image processing apparatus according toclaim 5, wherein the processor is configured to calculate a statisticalvalue obtained by dividing the pixel value of each of the pixels in thesurrounding pixels of a same color by the correction coefficient as theestimation value.
 7. The image processing apparatus according to claim1, wherein the processor is configured to calculate, for the surroundingpixel of a same color, the estimation value by solving simultaneousequations including a relational expression between the pixel value inthe surrounding pixel of a same color, the correction coefficient, andthe estimation value, established for each of the pixels.
 8. The imageprocessing apparatus according to claim 7, wherein the processor isconfigured to calculate, as the estimation value, a statistical value ofthe estimation value calculated by combining each of a plurality ofpixels of the surrounding pixels of a same color.
 9. The imageprocessing apparatus according to claim 1, wherein the processor isconfigured to calculate, in surrounding portions of thepixel-of-interest, the estimation value based on a ratio of a pixelvalue of each of the pixels in the surrounding pixels in which pixelvalues are not saturated to the correction coefficient of each of thepixels in the surrounding pixels of a same color.
 10. The imageprocessing apparatus according to claim 1, wherein the processor isconfigured to correct the pixel value of the pixel-of-interest using thecorrection amount such that the corrected pixel value of thepixel-of-interest falls from an average value of the pixel value of eachof the pixels in the surrounding pixels of a same color to the pixelvalue of the pixel-of-interest.
 11. The image processing apparatusaccording to claim 1, wherein the processor is configured to notsubtract the correction amount from the pixel value of thepixel-of-interest in a case where a difference between the valueobtained by subtracting the correction amount from the pixel value ofthe pixel-of-interest and the average value of the pixel value of thesurrounding pixels of a same color is larger than a predetermined value.12. An image processing method comprising: obtaining image datagenerated by an image sensor that forms a predetermined array patternusing color filters of a plurality of colors having mutually differentspectral transmittances and in which each of the color filters isarranged at a position corresponding to any of a plurality of pixels andobtaining a correction coefficient from a recording unit configured torecord, for each of the pixels, the correction coefficient forcorrecting a difference between pixel values corresponding to adifference between a spectral sensitivity and a preset referencespectral sensitivity in a predetermined wavelength range in apixel-of-interest; calculating an estimation value of a color componentas a correction target in the pixel-of-interest using a pixel value ofeach of pixels in a surrounding pixel of a same color and the correctioncoefficient, the surrounding pixel of a same color being a pixelsurrounding the pixel-of-interest and being a pixel including a colorfilter having a same color as a color filter arranged on thepixel-of-interest; calculating a correction amount of the pixel value ofthe pixel-of-interest based on the estimation value and the correctioncoefficient of the calculated pixel-of-interest; and correcting thepixel value of the pixel-of-interest based on the calculated correctionamount.
 13. A non-transitory computer-readable recording medium on whichan executable program is recorded, the program instructing a processorof an image processing apparatus to execute: obtaining image datagenerated by an image sensor that forms a predetermined array patternusing color filters of a plurality of colors having mutually differentspectral transmittances and in which each of the color filters isarranged at a position corresponding to any of a plurality of pixels andobtaining a correction coefficient from a recording unit configured torecord, for each of the pixels, the correction coefficient forcorrecting a difference between pixel values corresponding to adifference between a spectral sensitivity and a preset referencespectral sensitivity in a predetermined wavelength range in apixel-of-interest; calculating an estimation value of a color componentas a correction target in the pixel-of-interest using a pixel value ofeach of pixels in a surrounding pixel of a same color and the correctioncoefficient, the surrounding pixel of a same color being a pixelsurrounding the pixel-of-interest and being a pixel including a colorfilter having a same color as a color filter arranged on thepixel-of-interest; calculating a correction amount of the pixel value ofthe pixel-of-interest based on the estimation value and the correctioncoefficient of the calculated pixel-of-interest; and correcting thepixel value of the pixel-of-interest based on the calculated correctionamount.